
"""simple docstring"""


import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
SCREAMING_SNAKE_CASE			  =  logging.getLogger(__name__)



def 						lowerCamelCase__      (				)->				Any:







       """simple docstring"""
       UpperCamelCase       			=		argparse.ArgumentParser(
           description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."					)
       parser.add_argument("--file_path"		,   type=UpperCAmelCase_		,   default="data/dump.txt"		,   help="The path to the data."					)
       parser.add_argument("--tokenizer_type"		,   type=UpperCAmelCase_		,   default="bert"		,   choices=["bert", "roberta", "gpt2"]					)
       parser.add_argument("--tokenizer_name"		,   type=UpperCAmelCase_		,   default="bert-base-uncased"		,   help="The tokenizer to use."					)
       parser.add_argument("--dump_file"		,   type=UpperCAmelCase_		,   default="data/dump"		,   help="The dump file prefix."					)
       UpperCamelCase       			=		parser.parse_args()

       logger.info(F"Loading Tokenizer ({args.tokenizer_name})"					)
       if args.tokenizer_type == "bert":
              UpperCamelCase       			=		BertTokenizer.from_pretrained(args.tokenizer_name					)
              UpperCamelCase       			=		tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
              UpperCamelCase       			=		tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
       elif args.tokenizer_type == "roberta":
              UpperCamelCase       			=		RobertaTokenizer.from_pretrained(args.tokenizer_name					)
              UpperCamelCase       			=		tokenizer.special_tokens_map["cls_token"]  # `<s>`
              UpperCamelCase       			=		tokenizer.special_tokens_map["sep_token"]  # `</s>`
       elif args.tokenizer_type == "gpt2":
              UpperCamelCase       			=		GPTaTokenizer.from_pretrained(args.tokenizer_name					)
              UpperCamelCase       			=		tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
              UpperCamelCase       			=		tokenizer.special_tokens_map["eos_token"]  # `<|endoftext|>`

       logger.info(F"Loading text from {args.file_path}"					)
       with open(args.file_path		,   "r"		,   encoding="utf8"					) as fp:
              UpperCamelCase       			=		fp.readlines()

       logger.info("Start encoding"					)
       logger.info(F"{len(UpperCAmelCase_					)} examples to process."					)

       UpperCamelCase       			=		[]
       UpperCamelCase       			=		0
       UpperCamelCase       			=		1_00_00
       UpperCamelCase       			=		time.time()
       for text in data:
              UpperCamelCase       			=		F"{bos} {text.strip()} {sep}"
              UpperCamelCase       			=		tokenizer.encode(UpperCAmelCase_		,   add_special_tokens=UpperCAmelCase_					)
              rslt.append(UpperCAmelCase_					)

              iter += 1
              if iter % interval == 0:
                     UpperCamelCase       			=		time.time()
                     logger.info(F"{iter} examples processed. - {(end-start):.2f}s/{interval}expl"					)
                     UpperCamelCase       			=		time.time()
       logger.info("Finished binarization"					)
       logger.info(F"{len(UpperCAmelCase_					)} examples processed."					)

       UpperCamelCase       			=		F"{args.dump_file}.{args.tokenizer_name}.pickle"
       UpperCamelCase       			=		tokenizer.vocab_size
       if vocab_size < (1 << 16):
              UpperCamelCase       			=		[np.uintaa(UpperCAmelCase_					) for d in rslt]
       else:
              UpperCamelCase       			=		[np.intaa(UpperCAmelCase_					) for d in rslt]
       random.shuffle(rslt_					)
       logger.info(F"Dump to {dp_file}"					)
       with open(UpperCAmelCase_		,   "wb"					) as handle:
              pickle.dump(rslt_		,   UpperCAmelCase_		,   protocol=pickle.HIGHEST_PROTOCOL					)


if __name__ == "__main__":
      main()







"""simple docstring"""


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer



@dataclass
class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       torch.FloatTensor





class 	__a	(   _lowerCAmelCase     ,  _lowerCAmelCase						):






       @register_to_config
       def __init__(	self			:				int							,   UpperCAmelCase_			:				int = 3							,   UpperCAmelCase_			:				int = 3							,   UpperCAmelCase_			:				Tuple[str] = ("DownEncoderBlock2D",)							,   UpperCAmelCase_			:				Tuple[str] = ("UpDecoderBlock2D",)							,   UpperCAmelCase_			:				Tuple[int] = (64,)							,   UpperCAmelCase_			:				int = 1							,   UpperCAmelCase_			:				str = "silu"							,   UpperCAmelCase_			:				int = 3							,   UpperCAmelCase_			:				int = 32							,   UpperCAmelCase_			:				int = 256							,   UpperCAmelCase_			:				int = 32							,   UpperCAmelCase_			:				Optional[int] = None							,   UpperCAmelCase_			:				float = 0.18215							,   UpperCAmelCase_			:				str = "group"							,   )->	Optional[Any]:







              """simple docstring"""
              super().__init__()

              # pass init params to Encoder
              UpperCamelCase       			=		Encoder(
                  in_channels=UpperCAmelCase_							,   out_channels=UpperCAmelCase_							,   down_block_types=UpperCAmelCase_							,   block_out_channels=UpperCAmelCase_							,   layers_per_block=UpperCAmelCase_							,   act_fn=UpperCAmelCase_							,   norm_num_groups=UpperCAmelCase_							,   double_z=UpperCAmelCase_							,   )

              UpperCamelCase       			=		vq_embed_dim if vq_embed_dim is not None else latent_channels

              UpperCamelCase       			=		nn.Convad(UpperCAmelCase_							,   UpperCAmelCase_							,   1      )
              UpperCamelCase       			=		VectorQuantizer(UpperCAmelCase_							,   UpperCAmelCase_							,   beta=0.25							,   remap=UpperCAmelCase_							,   sane_index_shape=UpperCAmelCase_      )
              UpperCamelCase       			=		nn.Convad(UpperCAmelCase_							,   UpperCAmelCase_							,   1      )

              # pass init params to Decoder
              UpperCamelCase       			=		Decoder(
                  in_channels=UpperCAmelCase_							,   out_channels=UpperCAmelCase_							,   up_block_types=UpperCAmelCase_							,   block_out_channels=UpperCAmelCase_							,   layers_per_block=UpperCAmelCase_							,   act_fn=UpperCAmelCase_							,   norm_num_groups=UpperCAmelCase_							,   norm_type=UpperCAmelCase_							,   )






       @apply_forward_hook
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				torch.FloatTensor							,   UpperCAmelCase_			:				bool = True      )->	VQEncoderOutput:







              """simple docstring"""
              UpperCamelCase       			=		self.encoder(UpperCAmelCase_      )
              UpperCamelCase       			=		self.quant_conv(UpperCAmelCase_      )

              if not return_dict:
                     return (h,)

              return VQEncoderOutput(latents=UpperCAmelCase_      )






       @apply_forward_hook
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]							,   UpperCAmelCase_			:				torch.FloatTensor							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				bool = True      )->	Union[DecoderOutput, torch.FloatTensor]:







              """simple docstring"""
              # also go through quantization layer
              if not force_not_quantize:
                     UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.quantize(UpperCAmelCase_      )
              else:
                     UpperCamelCase       			=		h
              UpperCamelCase       			=		self.post_quant_conv(UpperCAmelCase_      )
              UpperCamelCase       			=		self.decoder(UpperCAmelCase_							,   quant if self.config.norm_type == "spatial" else None      )

              if not return_dict:
                     return (dec,)

              return DecoderOutput(sample=UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				torch.FloatTensor							,   UpperCAmelCase_			:				bool = True      )->	Union[DecoderOutput, torch.FloatTensor]:







              """simple docstring"""
              UpperCamelCase       			=		sample
              UpperCamelCase       			=		self.encode(UpperCAmelCase_      ).latents
              UpperCamelCase       			=		self.decode(UpperCAmelCase_      ).sample

              if not return_dict:
                     return (dec,)

              return DecoderOutput(sample=UpperCAmelCase_      )





